As mentioned in the introduction, most existing techniques included in clinical practice use shape and motion indices such as ejection fraction, ventricular volumes and myocardial thickening to classify the subjects under investigation. This means that a lot of information produced by the image is lost during this operation, in particular imaging evidence in relation to the tissue appearence in the blood pool, myocardium and right ventricle, as well as more complex morphological and functional information. But it is unclear which advanced indices could contribute to improved classification of cardiovascular cases. To address these issues, we propose a radiomics approach for computer-aided diagosis in cine-MRI. Radiomic features have been used so far mostly for cancer image quantification [1]}, [2]}, such as for the estimation of patient prognosis and treatment response based on the characteristics of the tumors as encoded by the image data. The suffix -omics attached to radiology refers to the use of large amounts of imaging features, from which the most relevant set can be selected for the specific task in question. In this paper, we estimate a large pool of new radiomic features from the segmented cine-MRI images, which will be then analyzed to extract the most powerful features for classification.
